Method and system for processing beamformed data

ABSTRACT

Examples relate to a method for processing beamformed data of a medium. The beamformed data includes a first set of beamformed data associated with a first spatial region and a second set of beamformed data associated with a second spatial region, and the method includes estimating the clutter caused by the second spatial region at the first set.

FIELD OF THE DISCLOSURE

The present invention relates to methods and systems for processingbeamformed data, in particular for medical imaging. In particular, themethod is suitable for providing image data of a medium scanned by atransducer device. For example, the method may be used in a device suchas for instance an ultrasound system.

BACKGROUND OF THE DISCLOSURE

It is known to use a plurality of transducer elements or transceivers(for example arranged as an array) for communication, imaging orscanning purposes, for example in the field of medical imaging, radar,sonar, seismology, wireless communications, radio astronomy, acousticsand biomedicine. One example comprises ultrasound imaging.

The aim of ultrasound imaging is to estimate the medium reflectivity. Ina conventional ultrasound imaging method, an ultrasound transducerdevice (also referred to as an ultrasound probe) with a set ofultrasound transducer elements may be used. In the method, one ormultiple transducers are used to transmit one or successively severalultrasound beam into a medium, corresponding to a transmission step.Then, in a reception step a set of backscattered echo signals arereceived from the medium by the set of transducer elements. Inparticular, each of the transducer elements converts a received echosignal into for example an electrical signal. The signal may further beprocessed by the ultrasound system. For example, they may be amplified,filtered digitalized and/or a signal conditioning step may be carriedout. The transducer elements may be arranged as a transducer array.

Conventionally, said signals are then transmitted to an image processingsystem. The received signals may be processed to generate image data ofthe scanned medium, for example using a beamforming method. Generally,beamforming may be understood as a signal processing techniqueconventionally used in sensor arrays for directional signal transmissionor reception. This process is used to generate beamformed data.

Complex media such as human soft tissues are made of uncountablescatterers. Due to the spatial extension of the incident beam, echoesgenerated by various scatterers may simultaneously be measured by theultrasound device. It means that the round-trip propagation timesrequired for an ultrasound wave to travel from the ultrasound device tothose scatterers forth and back are the same. The measured signals maythen result from the superimposed of various backscattered echoes. As aresults, a given area of the beamformed image which corresponds to aspatial region of the medium may be degraded by echoes that have beengenerated by a different region of the medium. This phenomenon called“clutter” may significantly impair the quality of beamformed data,potentially impacting the displayed images which can in turn result inworse medical diagnostics.

Holm, Synnevåg, and Austeng describe in the article “Capon BeamformingFor Active Ultrasound Imaging Systems”, 2009 IEEE 13th Digital SignalProcessing Workshop and 5th IEEE Signal Processing Education Workshop,Capon beamforming adapted to medical ultrasound imaging. Caponbeamforming has improved resolution, i.e. its ability to spatiallyseparate two targets close to each other, compared to traditionalbeamformers. Capon beamforming impacts the spatial resolution of thebeamformer, and a consequence is that it dramatically changes thespeckle statistics and the global aspect of the images.

Viola, Ellis, and Walker proposed an adaptive beamformer that aims atreducing the off-axis target impacts in their article “Time-DomainOptimized Near-Field Estimator for Ultrasound Imaging: InitialDevelopments and Results”, IEEE Transactions on Medical Imaging 2008.This beamformer suffers from its computational complexity and itdramatically impacts the speckle statistics as well.

Feder and Weinstein describe in the article “Parameter Estimation ofSuperimposed Signals Using the EM Algorithm”, IEEE Transactions OnAcoustics, Speech, And Signal Processing 1988, a computationallyefficient algorithm for parameter estimation of superimposed signalsbased on the Estimate Maximize (EM) algorithm. The algorithm decomposesobserved data into their signal components and then estimates theparameters of each signal component separately. The algorithm iteratesback and forth, using the current parameter estimates to decompose theobserved data better and thus increase the likelihood of the nextparameter estimates. The application of the algorithm to the multipathtime delay and to the multiple source location estimation problems isconsidered.

In the algorithm proposed by Feder and Weinstein, delay-and-sum DASbeamformer is presented as a maximum likelihood estimator of the signalbackscattered at a location that corresponds to a pixel of thebeamformed image. DAS beamforming is only applicable for a singlescatterer. A straightforward approach for the maximum likelihoodestimation of back scattered signal in that context is not numericallytractable. To cope with that problem, Feder proposed to use E-Malgorithm. This scheme is known to have sufficient convergenceproperties and has demonstrated its efficiency in such problems solving.

The algorithm of Feder and Weinstein is described in the context ofpassive imaging methods such as e. g. SONAR.

SUMMARY OF THE DISCLOSURE

Currently, it remains desirable to overcome the aforementioned problemsand in particular to provide a method and system for processingbeamformed data of a medium, in particular for estimating and/orcompensating clutter at a (first) selected set of beamformed data. It isthus desirable to provide a method and system which can improve thequality of beamformed data in a computationally efficient manner, forexample for facilitating image analysis, helping to set diagnostics,making diagnostics more reliable, and/or improving medical diagnostics.

Therefore, present disclosure relates to a method for processingbeamformed data of a medium. The beamformed data comprises a first setof beamformed data associated with a first spatial region and a secondset of beamformed data associated with a second spatial region. Themethod comprises: estimating (f) clutter caused by the second spatialregion at the first set.

In one example, a transducer device may be used for acquiring signaldata of the medium based on which the beamformed data may be obtained.

By providing such a method it becomes possible to estimate cluttercaused by the second spatial region at the first set which isrepresented by its associated first set of beamformed data. The methodmay advantageously in particular lead to a more reliable andcomputationally less complex estimation technique.

It is noted in this context that different beamforming techniques, forexample DAS (Delay and Sum) beamforming is sensitive to clutter. Theproposed method aims at reducing clutter while maintaining a processingtime compatible with real time imaging or data processing.

Moreover, the method may preserve speckle statistics.

Furthermore, the method does not require to iterate back and forthbetween beamformed data and signal data (for example acquired by atransducer device). Instead, the method may merely process thebeamformed data. Accordingly, the method is computationally lesscomplex, as it does not require back-projecting beamformed data tosignal data, i.e. to inverse the beamforming process, and then tobeamform the signal data again. Avoiding these calculation operations iscomputationally even more advantageous, in case the method is carriedout in a plurality of iterations. Hence, the clutter may be estimatedfor example in real-time or quasi real-time. Moreover, since the methodmay be computationally less expensive, the method might also requireless processing power and/or energy and/or computing time.

The method may operate on IQ or RF beamformed data (i.e. in-phase andquadrature phase, IQ, and/or radio frequency, RF, beamformed data). As afurther consequence, the method advantageously does not require anymodifications of the beamformer (for example a DAS beamformer) used toobtain the beamformed data.

Clutter may refer to a measurable quantity, such as an amplitude or anenergy of a received wave. For example, clutter may be one or severalreal values, or (for example in case the method processed beamformed IQdata) one or several complex values.

Clutter may denote for example a parasitic stray signal. Generally,clutter may refer to unwanted echoes in electronic systems, inparticular in the transducer device.

The method may further comprise: selecting (d) the first set anddetermining (e) the second set as a function of the location of theassociated second spatial region. In one example, the second spatialregion may be located such that the first set is susceptible for cluttergenerated at the second spatial region.

Additionally or alternatively the method may further comprise: selecting(d′) the second set and determining (e′) the first set as a function ofthe location of the associated first spatial region. For example, thefirst spatial region may be located such that the first set issusceptible for clutter generated at the second spatial region.

In other words, it is possible to determine one or several second setsbased on a selected first set, and/or to determine one or several firstsets based on a selected second set. Accordingly, both the first set andthe second set can be the starting point to determine the other one ofthe first set and the second. The method may comprise operations (d) and(e), or operations (d′) and (e′), or a combination of thereof. In thelatter case, for example clutter estimation at some first sets (forexample of image pixels) is processed using operations (d) and (e), andfor other first sets (for example of image pixels of the same or anotherimage) is processed using operations (d′) and (e′).

The operations (d) and (e) (and/or respectively (d′) and (e′)) may bepredetermined in advance, i.e prior to processing beamformed data of aparticular medium. For example, operations (d) and (e) (and/orrespectively (d′) and (e′)) may be predetermined as a function ofcharacteristics of a transducer device and/or of the shape of theincident beam. The respectively determined calculations may be (pre-)stored (for example in a lookup table or another type of mappingfunction, as described below). In other words, operations (d) and (e)(and/or respectively (d′) and (e′)) may merely depend on predefinedcharacteristics of the used transducer device, but not on a specificmedium. Merely operation (f) may depend on the medium.

As a consequence, the method may be carried out faster compared to priorart, as the calculations of operations (d) and (e) (and/or respectively(d′) and (e′)) may be read from a data storage. Hence, the clutter maybe estimated for example in real-time or quasi real-time. Moreover, themethod may be computationally less expensive and thus might require lessprocessing power and/or energy and/or computing time.

Generally, the beamformed data to be processed in the method maycomprise the first and the second set. In other words, the beamformeddata may comprise a plurality of sets of beamformed data, wherein eachset is associated with a respective spatial region.

A set of beamformed data (for example a first set, second set, etc.) mayrefer to or may constitute one or more pixels and/or voxels in thebeamformed data. The pixels and/or voxels may be represented by 2D imagedata or higher-dimensional image data or a 2D or higher-dimensionaltemporal sequence of image data, i.e. video data of arbitrary dimension.A set of beamformed data may also refer or may constitute a group orcluster of pixels or voxels.

It is noted that the numbering “first”, “second”, etc. of the sets ofbeamformed data are merely used to distinguish their function in themethod of the present disclosure. A given set which has the role of afirst set in one embodiment or iteration of the method may have the roleof a second set in another embodiment or iteration of the method. Forexample, assuming that the sets constitute pixels of an image, it may bedesirable to estimate the clutter at each pixel. However, the clutter ata given pixel is estimated as a function of other pixels of the sameimage. Therefore, the same pixel may be a “first set” in a oneembodiment or iteration of the method, but may be “a second set” inanother embodiment or iteration of the method. Hence, the terms “firstset” and “second set” could also be exchanged in the present disclosure.

The first and/or second spatial region(s) may be regions of the mediumor may be associated with regions of the medium. However, the firstand/or second spatial region(s) may also be defined as a function of aused transducer device. In the latter case, the regions may be definedindependently from a specific medium but merely as a function of thegeometry of the transducer device, in particular of its transducerarray.

In other words, a spatial region may correspond to a physical region ofscatterer(s) or reflector(s) in the medium, meanwhile a set ofbeamformed data may correspond to a position in image data.

The present disclosure may cover the scenario, in which a scattererlocated at a single second region directly causes clutter at the firstset. This phenomenon may arise if the first and second spatial regionare characterized by the same round trip propagation time. This time isdefined as the sum of the transmitted propagation time, i.e. the delayrequired for a given incident beam to travel from the ultrasound deviceto a spatial region of interest; and the received propagation time, i.e.the delay required for echoes generated at this spatial region ofinterest to travel back to a given transducer element of the ultrasounddevice.

However, also multiple scattering scenarios may be covered by thepresent disclosure. In such scenarios, multiple scatterers located atmultiple second spatial regions may generate echoes that have beenscattered multiple times and may cause clutter at the first set. Inother words, these echoes follow multiple scattered path that have thesame propagation time the echoes associated to the first spatial region.

Exemplary mediums comprise inert materials but also body part(s) such asa liver, a breast, muscles (muscle fibres) of a human or an animal. Inparticular, any strong reflectors such as medium interfaces (e.g. wallsof organs) can imply an increased reflectivity which might in returnlead to clutter at other regions.

The method may be applicable to any shape of emission waves. Inparticular, it can be apply to focused wave, diverging wave or planewave. Note that clutter generated by plane wave and diverging wave isusually more significant.

Beamforming may be referred to as a signal processing technique used toprocess signal data of a transducer device. Beamforming may inparticular be used to process RF signal data of a transducer device tocreate a spatial model of a medium, e. g. for obtaining image data ofthe medium, e. g. a human tissue.

Beamformed data may accordingly be data in the spatial domain, inparticular in a two or three dimensional (2D/3D) spatial domain, torepresent the medium. The signal data acquired by a transducer devicemay be in the time domain and/or in a space-time domain. For example,the signal data may be described according to two dimensions wherein onedimension reflects the acquisition time and the other one the spatiallocation of the transducer element of the ultrasound device thatacquired the signal.

The method may further comprise the following step:

-   -   compensating (g) the estimated clutter at the first set, and/or    -   removing (g′) the clutter at the first set.

By including the above method operations (g) and/or (g′), it becomespossible to compensate the estimated clutter at the first set and/or toremove clutter at the first set. This may allow to improve the qualityof the beamformed data by removing the impact of clutter. This can forexample facilitate diagnostics, make diagnostics more reliable, and/orimprove medical diagnostics.

The clutter may be estimated as a function of the location of the firstspatial region and/or of the location of the second spatial region. Forexample, if the location of both the first and the second spatial regionis taken into account, also a relative distance between these regionsmay be determined.

In one example, the locations of the first and/or second spatial regionmay be defined in relation to the position of the used transducerdevice.

The clutter may be estimated as a function of the second set and/or theamplitude of the second set. For example, an increased amplitude (i.e.an increased intensity or energy level) may lead to the estimation of anincreased clutter.

Generally, the second spatial region may be associated with the secondset. Accordingly, the second spatial region may be represented by thesecond set. Therefore, the clutter at the first set may be determined asa function of the second set.

In particular, the second set may be considered for estimating theclutter, (desirably only) in case the amplitude of the second setexceeds a predefined threshold. Accordingly, operation (e) may comprisea pre-selection and/or filtering of second sets with an increasedamplitude. This may simplify the method and reduce computational costsat similar or only slightly worse results as it is possible that onlysecond regions (as for example described below) having a reflectivityexceeding a predetermined threshold contribute significantly to theclutter at the beamformed data representing the first region. Otherregions may be disregarded in the estimation operation (f). It isfurthermore possible to consider first (and/or to prioritize) secondsets exceeding the threshold. Optionally, other second sets may beconsidered afterwards (and/or with less priority), for example to renderthe estimated clutter more accurate.

The second set of beamformed data may be associated to signal datareceived from the medium which is isochronous to signal data receivedfrom the medium associated with the first set. The signal data may be inthe time domain. The signal data may be hence RF signal data.

In other words, the second set of beamformed data may be associated tosignal data received from the medium which share a certain temporalrelation to signal data received from the medium associated with thefirst set, e.g. are isochronous.

Accordingly, the term isochronous may describe that for a first spatialregion, signals from the second set may be isochronous and may henceshare the same propagation time. Said propagation time may be i. e. atime period between transmitting a signal by the transducer device andreceiving a response signal. In particular, said propagation time mayrefer to a time period between a pulse emission by one or a plurality oftransducer elements and a reception of an echo signal from scatterer inthe medium by a transducer element. For example, each receivingtransducer element may be individually considered. The signalsassociated with the first and second set may have the same propagationtime, i.e. are isochronous, at the receiving transducer element. Hence,the signals associated with the second set can cause clutter at thefirst set, or in other words, the first set may be susceptible forclutter generated at the second region.

Performing ultrasound measurements may comprise transmitting an emittedsequence ES of ultrasound waves into the medium, receiving a responsesequence RS of ultrasound waves from the medium, wherein the ultrasoundsignal data may be based on the response sequence RC of ultrasoundwaves.

In case the second set of beamformed data may be associated to signaldata received from the medium which is isochronous to signal datareceived from the medium associated with the first set, this may allowto estimate clutter that is related to the emitted sequence ofultrasound waves.

Determining (e) the second set of beamformed data may comprise:determining (e) a plurality of second sets of beamformed datarespectively associated with a plurality of second spatial regions thatare located such that the first set is susceptible for clutter generatedat the second spatial regions.

For example, based on a selected first set (for example an image pixel),a plurality of second sets (for example further adjacent pixels) may bedetermined.

Estimating (f) the clutter at the first set may comprise: estimating (f)a plurality of clutter contributions respectively associated to thesecond sets, the clutter at the first set being a function of theplurality of clutter contributions.

In particular, the plurality of second spatial regions may be differentto each other, i.e. may be at different locations of the medium. Hence,clutter contributions caused by different (second) regions of the mediummay be considered to estimate a total or summed clutter.

Furthermore, this allows a clutter estimation that includes cluttercontributions of a plurality of second spatial regions, e. g. aproportion of or all second spatial regions that are located such thatthey can cause clutter at the first set.

The (total or summed) clutter at the first set may be estimated by alinear combination of the plurality of clutter contributions. Forexample, clutter contribution from a second spatial region being fareraway from the first region may be weighted less than cluttercontribution from a second spatial region being closer to from the firstregion. The closer second region may namely have a stronger influence.Accordingly, the resulting estimation of the total clutter can becomemore accurate.

For example, the linear combination/c may be expressed by the followingequation (1):

lc=Σ _(i) a(i)*b(i)  (1),

wherein i refers to a selected second spatial region, a(i) denotes thecomputed clutter contribution associated to the spatial region i andb(i) denotes a respective weighted coefficient.

According to a further example, the weighted coefficients may bedetermined based on a mathematical model that may be based on relevantlaws of physics and/or parameters that describe aspects of the datataking, such as aspects related to the transducer device and/or themedium and/or the emitted signal and/or the received signal.

In a corresponding manner, and in particular with reference tooperations (d′) and (e′), determining (e′) the first set of beamformeddata may comprise: determining (e) a plurality of first sets ofbeamformed data respectively associated with a plurality of firstspatial regions that are located such that each first set is susceptiblefor a clutter contribution generated at the second spatial regions.

Estimating (f′) the clutter contribution at each first sets maycomprise: estimating (f′) a plurality of clutter contributionsrespectively associated to the first sets. Accordingly, based in theselected second set, the clutter contribution caused by the secondspatial region associated with said second set may be determined at theconcerned first sets. A total clutter at any one of the first sets maythen be calculated by summing clutter contributions caused by differentsecond spatial regions.

The method may further comprise before selecting (d, d′) the firstand/or the second set: processing (c) ultrasound signal data of themedium to obtain the beamformed data. Instead of ultrasound signal dataalso any other type of signal data originating from a transducer devicemay be used.

The method may further comprise, before processing (c) ultrasound signaldata or selecting (d) beamformed data, transmitting (a) an emittedsequence (ES) of ultrasound waves (We) into the medium (11), andreceiving (b) a response sequence (RS) of ultrasound waves (Wr) from themedium. The ultrasound signal data may be based on the response sequence(RS) of ultrasound waves (Wr).

Determining (e) the first and/or second set and/or estimating theclutter (f) may be further based on at least one of:

-   the geometry of a transducer device (in particular of its transducer    array) used for acquiring data of the medium on which the beamformed    data are based,-   the arrangement and/or size (for example the width) of the single    transducer elements of the transducer device,-   any further predefined characteristics of the transducer elements,    for example predefined characteristics of their respective piezo    elements,-   the emission and/or receive aperture of the transducer device,-   the emission duration,-   the wavelength and/or type of emission pulse (or respective emitted    wave or respective acoustic beam) on which the beamformed data is    based,-   predefined characteristics (for example the geometry) of the emitted    wave front (for example the angle of an emitted planar wave with    regard to the emitting transducer device), and-   a predetermined speed of sound model of the medium that has been    used for the beamforming process.

The above-mentioned characteristics may also be referred to aspredefined parameters of the acquisition sequence. This may allow toobtain a more accurate and/or more precise estimation of clutter. Inparticular, by taking into account at least one of these predefinedparameters of the acquisition sequence, for any selected first set, allsecond sets may be (pre-)determined and for example stored in a lookuptable or the like.

Each set of beamformed data may be associated with at least one pixel orvoxel.

The beamformed data may be beamformed IQ data (i.e. in-phase andquadrature phase, IQ, data) and/or beamformed RF data (i.e. radiofrequency, RF, data).

Accordingly, a set of beamformed data may for example comprise orconsist of at least one set of an in-phase and a quadrature phase value.

In case the beamformed data are arranged in the form of a two- orthree-dimensional matrix, each pixel (in two dimensions) or each voxel(respectively in three dimensions) may comprise or may be defined by arespective set of beamformed data.

The first and/or second set and/or the first and/or second spatialregion may be predetermined. For example, the first and/or the secondspatial region may be predetermined as a function of any of theabove-mentioned parameters of the acquisition sequence (for example itsgeometry).

The relation between the first and the second spatial region (i.e.position(s) of second spatial region(s) for a selected first spatialregion) may be stored in a respective mapping function, e.g. in a lookuptable. In other words, operations (d) and (e) (and/or respectivelyoperations (d′) and (e′)) may be predetermined/pre-calculated andstored, e.g. in look up tables/mapping functions, independently of thespecific beamformed data of the medium. When the method is applied to aspecific medium, the selection of the second spatial region for a givenfirst set and/or the selection of the first spatial region for a givensecond set may be determined by using said mapping function.

The method may be performed for a plurality of first sets, in paralleland/or in series.

This may allow to estimate clutter at a plurality of first sets, e. g.for a part of or for the entirety of the beamformed data. The part maybe for example a region of interest in the beamformed data. Afterestimation, the clutter may be compensated at the first sets. Theseameliorated beamformed data may then be graphically represented and/orfurther processed.

The method may be performed for the first spatial region (or theplurality of first sets, as described above) in several iterations. Ateach iteration, modified beamformed data may be obtained by compensatingthe estimated clutter, in particular at the plurality of first sets. Themodified beamformed data obtained in a first iteration is used in asubsequent second iteration. In a first default example, the method maycomprise one or more iterations. In a further example, the method maycomprise a plurality of iterations with a range of two to seven. Withtwo iterations, the estimation may already be enhanced, and more thanseven iterations do not necessarily lead to a significant ameliorationin view of the additional computational costs. In a more specificexample, the method may comprise three iterations, what appears to be anadvantageous tradeoff between optimisation of the clutter estimation andlimitation of the required computational costs.

Accordingly, with each iteration, the clutter may be increasinglycompensated.

The number of applied iterations may be fixed and/or any predefinedconverging rule and/or threshold may be used to determine the number ofiterations or to define a stop criteria.

An AI (Artificial Intelligence) based model, for instance amachine-learning model like for example a neural network, may be trainedbased on an estimated clutter. The machine-learning model may be furthertrained based on any essential and/or optional feature of the methoddescribed above, as e. g. location of first and/or second spatialregion, reflectivity of the second spatial region, geometry of atransducer device used for acquiring data of the medium on which thebeamformed data are based, the arrangement of the signal transducerelements of the transducer device, the emission and/or receive apertureof the transducer device, the wavelength and/or type of emission pulseon which the beamformed data is based, the geometry of the emitted wavefront, look-up tables that store regions from which clutter isgenerated, etc.

The AI based model may be used for estimating clutter and/orcompensating and/or removing an estimated clutter.

Using an AI based model for estimating clutter and/or compensatingand/or removing an estimated clutter may provide results of a similarquality at reduced computational costs.

A trained AI-based model may be evaluated by using beamformed dataassociated with planar waves for scanning a medium as input data, and bycomparing the output data of the AI model with beamformed dataassociated with focalized waves for scanning the same medium as outputdata. Since the beamformed data associated with focalized waves are lessimpacted by clutter, they may provide a good reference for the outputdata of the AI-based model.

Generally, two types of acquisitions of a medium may be used to obtaintwo types of bdeamformed data respectively, i.e. beamformed dataassociated with (1.) acquisitions using plane waves, and (2.)acquisitions using focalized waves of the same medium.

According to an example, the method of the present disclosure may beapplied to beamformed data of both (1.) and (2.). The results ofestimated clutter and in particular of the compensation of clutter maythen be compared, for example for evaluating the efficiency of themethod. In this case, the compensation of clutter should be much moresignificant at (1.) and should converge towards the beamformed data of(2.).

It is also possible to obtain several times beamformed data of (1.) andonce of (2.) of the same medium. The beamformed data of (2.) may be usedto calibrate and/or validate the compensation method which compensatesthe several beamformed data of (1).

The present disclosure further relates to a computer program comprisingcomputer-readable instructions which when executed by a data processingsystem cause the data processing system to carry out the method may beused.

The present disclosure further relates to a system for processingbeamformed data of a medium. The beamformed data comprise a first set ofbeamformed data associated with a first spatial region and a second setof beamformed data associated with a second spatial region. The systemcomprises a processing unit configured to:

-   estimate (f) the clutter caused by the second spatial region at the    first set.

The system may comprise the transducer device. The system may comprisein particular a probe (for example an ultrasound probe) which maycomprise the transducer device(s).

The system may be an ultrasound system.

The system may comprise further functional characteristics and/or may beconfigured in correspondence to the method operations described above.

The disclosure and its embodiments may be used in the context of medicalsystems dedicated to human beings, plants or animals but also any(non-living) soft material to be considered.

It is intended that combinations of the above-described elements andthose within the specification may be made, except where otherwisecontradictory.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory only,are provided for illustration purposes and are not restrictive of thedisclosure, as claimed.

The accompanying drawings, which are incorporated in and constitute apart of this specification, are provided for illustration purposes, andillustrate embodiments of the disclosure and together with thedescription and serve to support and illustrate the principles thereof.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an exemplary embodiment of a method according toembodiments of the present disclosure;

FIG. 2 shows a system carrying out a method according to an exemplaryembodiment of the present disclosure;

FIG. 3 shows an example of RF signal data of a medium with threereflectors;

FIG. 4 shows the beamformed data of the RF signal data of the FIG. 3 ;

FIG. 5 shows the principles of a first example of a method forcompensating clutter; and

FIG. 6 shows the principles of a second enhanced exemplary embodiment ofa method for compensating clutter according to the present disclosure.

DESCRIPTION OF THE EMBODIMENTS

Reference will now be made to exemplary embodiments of the disclosure,examples of which are illustrated in the accompanying drawings. Whereverpossible, the same reference numbers will be used throughout thedrawings to refer to the same or like parts. Moreover, the featuresexplained in context of a specific embodiment, for example that one ofFIG. 1 , also apply to any one of the other embodiments, whenappropriate, unless differently described.

FIG. 1 shows an exemplary embodiment of a method according toembodiments of the present disclosure. The method may be carried out bymeans of a system 1, more in particular by an ultrasound system 20. Anexample of an ultrasound system is described in context of FIG. 2 .

The method may be an ultrasound method carried out by an ultrasoundsystem. Possible ultrasound methods comprise B-mode imaging, shear waveelastography imaging (such as ShearWave® mode developed by theapplicant, Doppler imaging, M mode imaging, CEUS imagine, Ultrafast™Doppler imaging or angio mode named under Angio P.L.U.S™ ultrasoundimaging or any other ultrasound imaging mode. Accordingly, differentacquisition modes may be used to obtain signal data based in which thebeamformed data may be determined. The method may be part of any of theabove-mentioned methods or may be combined with any of these methods.

However, the method according to the present disclosure may also beapplied to other technical fields than ultrasound examination. Inparticular, any technical field is possible which uses a plurality oftransducer elements to acquire data/signals of an examined medium orenvironment and/or which may use a beamforming technique based on thecollected data/signals. Examples comprise methods using a radar system,sonar system, seismology system, wireless communications system, radioastronomy system, acoustics system, Non-Destructive Testing (NDT) systemand biomedicine system or any other technique in the field of activeimaging. The principle of active imaging, i.e. of emitting pulses into amedium via one or several elements (sources) and receiving responsepulses via one or several elements (receiver) and to estimate and/orcompensate a clutter is similar to the functionalities of an ultrasoundtransducer.

Accordingly, the method according to the present disclosure may in eachof these cases achieve the same positive technical effects as describedabove, for example of compensating undesired clutter at beamformed data.However, for mere illustration purposes of the present disclosure, inthe following it is referred to the example of an ultrasound method.

The method may be for example a method for compensating clutter inbeamformed data of a medium, and more in general for processingbeamformed data.

In an optional operation (a) at least one pulse is transmitted into amedium. For example, the transmission step may comprise insonificationof the medium with a cylindrical wave that focuses on a given pointand/or plane waves of different angles. More in particular, in thetransmission step a plurality of ultrasonic waves may be transmittedinto an imaged region.

Generally, in the present disclosure a pulse may correspond to anacoustic or electrical signal emitted by a transducer element. The pulsemay for example be defined by at least one of: the pulse duration, thefrequency of the resulting wave, the number of cycles at the givenfrequency, the polarity of the pulse, etc. A wave may correspond to thewavefront generated by one or several transducer elements (i.e. byrespectively emitted pulses). The wave may be controlled by means ofemission delay between the different used transducer elements. Examplescomprise a plane wave, a focused wave and a divergent wave. A beam maycorrespond to the physical area insonified by the wave (for example inthe medium). Hence, the beam may be related to the wave but may haveless or no temporal notion. For example, it may be referred to a beamwhen the depth of field of a focused beam is of interest.

In an optional operation (b), a response sequence is received from themedium by the set of transducer element(s). The response sequence maycomprise backscattered echoes of the insonification of operation (a).The response sequence may also be referred to as signal data, inparticular ultrasound signal data and/or RF and/or IQ signal data. Thesignal data may be in the time domain, more in particular in aspatio-temporal domain, as for example described in more detail below.In one example, the response sequence may be processed by bandpassfiltering, in order to keep only one or several frequency ranges.

In an optional operation (c), the response sequence is processed toobtain beamformed data. Beamformed data may be data in the spatialdomain, in particular in a two- or three-dimensional spatial domain, torepresent characteristics of the medium. For example, in the case ofB-mode imaging, the beamformed data is an estimation of the mediumreflectivity. In one example, in case a plurality of beamformed datacollections are obtained for a respective plurality of frequency ranges(as explained above), the beamformed data may be defined as a functionof frequency.

It is noted that operations (a) to (c) are optional, as they may also becarried out by any other system than the system used for operations (d)to (f) or at another time. Data may also be provided by otherfunctionalities such as simulation devices, insonification on a phantom,etc. It is also possible that the beamformed data are pre-stored, andfor example provided by/read on a data storage, a communicationinterface, etc.

In optional operation (d) a first set of beamformed data associated witha first spatial region of the medium is selected. It is also possible toselect a second set of beamformed data associated with a second spatialregion of the medium in an optional operation (d′). Said selection maybe controlled by a predefined selection algorithm, as for exampledescribed in more detail below.

In optional operation (e) (in particular following operation (d)) asecond set of beamformed data associated with a second spatial region ofthe medium is determined. Said second region is located such that it maycause clutter at the first set, or in other words, such that the firstset is susceptible for clutter generated at the second spatial region.Accordingly, based on the location of the first and second spatialregion, it may be determined, in operation (e), whether the (second)region would generally be able to cause clutter at the first set or not.In case operation (d) is replaced by operation (d′), it is furtherpossible in an optional operation (e′) to determine a first set ofbeamformed data associated with a first spatial region of the medium.The further features of the method may be adapted, respectively.

Optionally, operations (d) and (e) or both of them may be carried out inadvance. In other words, these operations may be carried out once for agiven transducer device, a given acquisition sequence and a givenbeamforming process and may then be valid for any medium. This ispossible, since these operations does not depend on characteristics of aspecific medium. Therefore, the calculations of these operations may bestored for the specific transducer device, acquisition sequence andbeamforming process. Once the method is applied to specific medium, thecalculations of these operations (d) and (e) may be read from a datastorage. It is also possible to store respective calculations fordifferent types of transducer devices.

In one example, for each first region the respectively (pre-)determinedsecond regions may be stored in a look-up table or other form ofmapping. The look-up table may be usable right after determination or inthe future, locally or remotely.

In operation (f) the clutter caused by the second spatial region at thefirst set is estimated. Accordingly, in this operation, it is estimated,whether the second spatial regions actually cause clutter or not, andoptionally also to which extent (i.e. amplitude or/and amount ofclutter).

As stated above, for a given first spatial region and speed of soundmodel used for the beamforming process, there may be multiple secondspatial regions that are susceptible to cause clutter at the first setfor a given emission and a given received transducer. In somebeamforming process, signals generated by multiple emitted waves andmeasured by multiple transducers are used to generate the first set ofbeamformed data. In this case, clutter contribution arises from multiplesecond spatial regions may be estimated for each emitted waves andreceived transducer used to beamform the first set.

According to a first option, the operations (e) and (f) may be repeatedvia loop L1 over the spatial second regions for a given emitted wavesand received transducer and may hence be carried out for severaliterations. In each iteration a different second spatial region may bedetermined in operation (e) and a respective clutter contribution ofsaid second region may be estimated in operation (f). Accordingly, inone example, a total or summed clutter at the first set may be estimatedby a linear combination of the plurality of clutter contributions.

According to a second option, the operations (e) and (f) may be repeatedvia a further loop L2 over the receiving transducer elements and mayhence be carried out for several iterations. In each iteration, anensemble of second spatial regions may be determined for a given(different, receiving) transducer element of the transducer device usedfor determining the first set of beamformed data. A respective cluttermay be estimated for said transducer element in operations (e) and (f).As shown in FIG. 2 , the method may namely be applied to a singletransducer element. The method may hence be repeated to consider aplurality of transducer elements. Said plurality of transducer elementsmay comprise all transducer elements of the transducer device or onlythose transducer elements whose signal data are used for determining thefirst set of beamformed data. The loop L2 may comprise the loop L1. Inother words, in each iteration of loop L2, the iterations according toloop L1 may be included.

According to a third option, the operations (e) and (f) may be repeatedvia loop L3 over emitted waves and may hence be carried out for severaliterations, for example in case of synthetic beamforming. In eachiteration, another emitted wave may be considered. Hence, the operations(e) and (f) may be iterated over the number of transmitted waves used tobeamform the first set. A wave may be generated by one or severaltransducer elements. For example, the transducer device may generateplanar emission waves with different predefined emission angles. A beammay also be referred to as the area through which the sound energyemitted from the transducer device travels. The loop L3 may comprise atleast one of or all of the loops L1 and L2. In other words, in eachiteration of loop L3, the iterations according to loops L1 and L2 may beincluded.

Note that loop L1, L2, L3 may be processed in various order and combinedin order to estimate a total clutter at the first set that arises fromthe combination of clutter contributions generated by each one of thesecond spatial regions determined by the loop L1, L2 and L3.

It is also possible that at least one of loops L1 to L3 comprises aniteration from operation (e) to operation (g) (or alternatively (g′)),instead of from operation (e) to operation (f). Accordingly, in eachiteration, the estimated clutter may be compensated and/or removed inoperation (g),(g′).

In an optional operation (g) the estimated clutter is compensated at thebeamformed data. In particular, in an optional operation (g′) theclutter may be removed at the beamformed data. It is however alsopossible that the clutter is compensated only in part.

According to a fourth option, the operations (d) to (g) (oralternatively (g′)) may be repeated via loop L4 and may hence be carriedout for several iterations. In each iteration a different first setassociated with a respectively different first spatial region may beselected in operation (d). A respective clutter caused by a determinedsecond spatial region may be estimated in operation (f) and compensatedand/or removed in operation (g), (g′). For example, a predefinedselection algorithm may select different first regions on a coordinatesystem of the beamformed data, for example in a stepwise manner. In thisway, clutter may be estimated across a spatial region of interest in thebeamformed data or across the complete spatial extension of thebeamformed data. The loop L4 may comprise at least one of or all of theloops L1 to L3. In other words, in each iteration of loop L4, theiterations according to loops L1 to L3 may be included.

It is also possible that loop L4 comprises an iteration from operation(e) to operation (f), instead of from operation (e) to operation(g),(g′). Accordingly, in each iteration of loop L4, the estimatedclutter may be merely estimated in operation (f). Once the clutter hasbeen estimated for the plurality of first sets (for example for theentirety of beamformed data), the clutter may be compensated and/orremoved respectively for the plurality of first sets in operation(g),(g′).

It is further possible that the iterations of at least one of loops L1to L4 are parallelly processed.

In case operations (d) and (e) are replaced by operations (d′) and (e′),the iterations of loops L1 to L4 may be adapted by respectivelyexchanging the first set by the second set and the second set by thefirst set.

In an optional operation (h) processed beamformed data may be obtained.This may in particular be the case, once the iterations of (at least oneof or all of) loops L1 to L4 are terminated. As a result, the entiretyof beamformed data may be processed. For example, the processedbeamformed data may be displayed (for instance to a user of the systemdescribed in context of FIG. 2 ) and/or may be further processed. Forexample, the processed beamformed data may be provided to another systemor module, for instance an algorithm or AI-based model.

According to a fifth option, the operations (d) to (h) may be repeatedvia loop L5 and may hence be carried out for several iterations. In eachiteration processing of the beamformed data according to operations (d)and (h) may be repeated. Accordingly, the loop L5 may comprise at leastone of or all of the loops L1 to L4. In other words, in each iterationof loop L5, the iterations according to loops L1 to L4 may be included.At each iteration, modified beamformed data may be obtained byprocessing the beamformed data obtained in a previous iteration. Inother words, the modified beamformed data obtained in a first iterationmay be used in a subsequent second iteration. Accordingly, with eachiteration, the clutter may be more accurately estimated and compensated.

The method may also be carried out using any combination of loops L1 toL5.

FIG. 2 shows a system carrying out a method according to an exemplaryembodiment of the present disclosure.

The system 100 may for example be configured to obtain and processbeamformed data of a medium 11, or for instance for the purpose ofimaging an area in a medium 11.

The medium 11 is for instance a living body and in particular human oranimal bodies, or can be any other biological or physic-chemical medium(e.g. in vitro medium). The medium may comprise variations in itsphysical properties. For example, the medium may comprise a liver,breast, muscles (muscle fibers), and in particular any interfaces in themedium (e.g. walls of organs). Such interfaces can namely have anincreased reflectivity which might in return lead to clutter at otherregions.

The system 100 may include a probe 12 comprising at least a transducerdevice, for example an ultrasound transducer device. Said transducerdevice may comprise one or a plurality of transducer elements 20, forexample in the form of a transducer array arranged along an x-axis. Eachtransducer element 20 may be adapted to transform a signal into anultrasound wave (emit) and/or to transform an ultrasound wave into asignal (receive).

The system 100 may further include an electronic processing unit 13.Said unit may optionally control the transducers in the probe in anymode (receive and/or emit) in the case the same probe is used foremission/reception. Different probes may also be used, either foremission/reception or for appropriate adaptation to scanned medium. Emitand receive transducer elements may be the same, or different ones,located on one single probe or on different probes.

Furthermore, the unit 13 may process ultrasound signal data, anddetermine characteristics of the medium and/or images of saidcharacteristics.

The probe 12 may comprise a curved transducer so as to perform anultrasound focusing to a predetermined position in front of the probeinto a direction of a z axis. The probe 12 may also comprise a lineararray of transducer. Moreover, the probe 12 may comprise few tens oftransducer elements up to several thousand (for instance 128, 256, or 8to 2064) juxtaposed along an x axis so as to perform ultrasound focusinginto a bi-dimensional (2D) plane. The probe 12 may comprise abi-dimensional array so as to perform ultrasound focusing into atri-dimensional (3D) volume. Moreover, the probe may also compriseseveral transducer devices, for example at least one for emission and atleast one for reception. In another example, the probe 12 may comprise asingle transducer element. In another example, the probe 12 may comprisea transducer device in a matrix form (comprising in this case forexample up to several thousand transducer elements).

The above processing unit 13 and the probe 12 may be configured to sendan emitted sequence ES of ultrasound waves We into the medium 11, usingfor example one transducer elements 20 or a predefined group oftransducer element 20. The above processing unit 13 and the probe 12 mayfurther be configured to receive a received sequence RS of ultrasoundwaves (i.e. ultrasound signal data) from the medium, using for exampleone transducer element 20 or a predefined group of transducer elements20 (the same or another than that one used for emission).

The ultrasound waves We, Wr toward and from the location may be afocused wave (beam) or a non-focused beam. In this context, apre-defined beamforming method may be used, for example: The emittedultrasound wave We may be generated by a plurality of transducerssignals that are delayed and transmitted to each transducer of atransducer array. The received ultrasound wave Wr may be composed of aplurality of transducer signals that are combined by delay and summationto produce a received sequence RS.

In a possible embodiment of the method of FIG. 1 specific transducerelement 20 a may be considered. A clutter caused by a second spatialregion r2 at a first set of beamformed data associated with a firstspatial region r1 may be estimated.

As shown in FIG. 2 , for a given emission wave (or the respectiveacoustic beam) the echo signal from both regions r1 and r2 may have thesame round trip propagation time for the received transducer element 20a. Accordingly, the signal data received from the first region r1 may beisochronous to signal data received from second region r2. Hence, sincethe signals associated with the respective first and second set have thesame propagation time for the transducer element 20 a, the signalsassociated with the second set can potentially cause clutter at thefirst set. In a simplified manner, it may be said that region r2 isisochronous to region r1 for a given transducer element and a givenemission wave.

Due to this isochronous characteristic, the second set of beamformeddata associated with the second spatial region r2 is located such thatthe first set is susceptible for clutter generated at the second spatialregion r2. In other words, an area (or location) may be determined onwhich any second regions are located which can generate clutter at thefirst set. In one example, said area may have the form of a parabola p1(for example in case of a planar emission wave). However, the area mayalso have any other form, for example of an ellipse. Generally, the areamay be determined as a function of at least one of the selected firstset, the considered transducer element, the geometry of the transducerdevice (or more in particular its transducer array), the emission wave(or the respective acoustic beam), and a predetermined propagation speedmodel of the medium.

In one example, the propagation speed c may be assumed to be constant inthe medium. In another example, the propagation speed c may bedetermined by a propagation speed model. If for example the medium isknown, speed values may be attributed to different areas of the medium,for instance to muscles, etc.

It may be assumed in the present disclosure that the size of thetransducer element may be relatively small in comparison to thewavelength of the emitted waves and/or their spatial pulse length. Thespatial pulse length of an emitted wave may also determine the width ofthe above-mentioned area, i.e. of the exemplary parabola p1 on whichsecond regions are located.

In order to estimate the clutter (in particular to evaluate whetherthere exist really clutter caused by r2), the second set of beamformeddata associated with r2 may be taken into account, in particular theamplitude (or energy level) of said second set. In other words, theclutter may be estimated as a function of the second set and/or theamplitude of the second set. It is further possible to take the positionof the r1 and r2 into account. For example, the closer they are locatedto each other and/or the closer the second region r2 is to a pointdirectly ahead of the considered transducer element (in the directionz), the more the clutter contribution of said second region r2 may beweighted. Furthermore, in case the amplitude of the second set does notexceed a predefined threshold, r2 may be completely disregarded in theestimation operation. Generally, characteristics of r1 and r2 (forexample their respective amplitude or location) may be determined in theassociated beamformed data, i.e. in the first and second set ofbeamformed data.

A corresponding exemplary scenario is shown for transducer element 20 bin view of spatial regions r1 and r3 being located on a parabola p2relevant for the transducer element 20 b. Accordingly, for transducerelement 20 b the clutter caused by the third spatial region r3 may beestimated at the first set. In other words, in order to estimate theclutter at the first set, a plurality of transducer elements 20 a, 20 bmay be considered. Said plurality of transducer elements may compriseall transducer elements of the transducer device or only thosetransducer elements whose signal data are used for determining the firstset of beamformed data (cf. also the iteration over loop L2, asexplained above).

FIG. 3 shows an example of RF signal data of a medium composed by threereflectors. The RF signals have been measured in this example by alinear array and results from the insonification of this medium with aplane wave of incident angle equal to 0 with regards to the ultrasoundarray. In this example the RF signal data are in the form of atwo-dimensional matrix. The signal data may be in the time domain ormore in particular in a space-time domain. The signal data may havehence two dimensions wherein one dimension (in FIG. 3 the vertical axis)reflects the acquisition time and the other dimension (in FIG. 3 thehorizontal axis) reflects the spatial axis of the transducer array ofthe used transducer device (i.e. in FIG. 2 the X-axis). Said signal dataof FIG. 3 may be acquired by a transducer device, as for example shownin FIG. 2 .

The example of FIG. 3 shows signal data received from a medium withthree point reflectors leading to three arched signal responses 31 a, 31b, 31 c.

FIG. 4 shows the beamformed data of the RF signal data of the FIG. 3 .Said beamformed data are in the spatial domain. The beamformed data mayhave two dimensions wherein one dimension (in FIG. 4 the vertical axis)reflects the depth direction of the medium (i.e. in FIG. 2 the Z-axis)and the other dimension (in FIG. 4 the horizontal axis) corresponds tothe axis of the transducer array of the used transducer device (i.e. inFIG. 2 the X-axis).

The beamformed data may be obtained by a Delay And Sum (DAS) beamformer,as shown in equation (2):

$\begin{matrix}{{o\left( {x,z} \right)} = \frac{\sum_{n = 0}^{N - 1}{\alpha_{n}{s_{n}\left( {t - \tau_{n}} \right)}}}{\sum_{n = 0}^{N - 1}\alpha_{n}^{2}}} & (2)\end{matrix}$

where:

-   τ_(n) is the estimated round-trip propagation time for the incident    wave to travel to point (x,z) and to back-propagates toward the    transducer element n, and-   α_(n) is the apodization coefficient linked to (x, z) and transducer    element n

An example of an optimum result of the beamformed data is shown in FIG.4 which illustrates the three beamformed pixels 40 a, 40 b, 40 c (eachone highlighted by a circle), which represent the reflectors in themedium.

However, the DAS beamformer is optimal in case of a single pointreflector only. Therefore, in the example of FIG. 3 , the multiple (i.e.in FIG. 3 three) point reflectors result in clutter blurring images anddegrading contrast. This may be in particular due to the overlappingsections 30 of the arch signals 31 a, 31 b, 31 c. As a consequence, thebeamformed data of FIG. 4 would in reality be deteriorated by clutter.

FIG. 5 shows the principles of a first example of a method forcompensating clutter. In particular, FIG. 5 shows four stages of anexemplary Expectation-Maximization (E-M) method for compensatingclutter. The used E-M algorithm may enable to separate the not-tractableproblem of likelihood maximization into parallel easy likelihoodmaximizations.

In stage 51 beamformed data are shown (obtained for example in operation(c) of the method of FIG. 1 ) which may comprise undesired clutter.

In stage S2 the beamformed data may be transformed back into RF signaldata. Accordingly, the pixels of the beamformed data matrix may beback-projected to the RF data matrix, i.e. to inverse the beamformingprocess.

In stage S3, for a plurality of different spatial regions or for eachspatial region of the medium, modified RF signal data may be built byremoving the contribution of other (or all other) spatial regions. Saidoperation may be referred to as the E (estimation) step. In the exampleof FIG. 5 , the contribution of the reflectors associated with pixels 40a, 40 c, i.e. arches 31 a, 31 c are “removed”.

In stage S4 the modified RF signal data is beamformed, to obtain thebeamformed data of isolated pixel 40 b. A regular DAS beamformer may beused in for this purpose. Said operation may also be referred to as theM (Mximisation) step.

The E-step and the M-step may be repeatedly performed in a plurality ofiterations. As starting point in the first iteration the first E-stepmay use conventional beamformed data (cf. stage 51), and the subsequentE-step may be based on the beamformed data obtained in the precedingM-step (cf. stage S4).

Every iteration of the method may result in a modified RF data matrixbuilding step (cf. stage S3), based on the current image estimatefollowed by a regular DAS beamforming (cf. stage S4) operated on themodified RF data matrix.

The method of FIG. 5 may decouple the complicated multiparameteroptimization problem of image beamforming while taking into accountoff-axis signals into N separate maximum likelihood optimizations, withN being the total number of spatial regions. However, the method of FIG.5 iterates back and forth between beamformed data (i.e. parameterestimates) and RF signal data (i.e. observed data), which iscomputationally expensive.

FIG. 6 shows the principles of a second enhanced exemplary embodiment ofa method for compensating clutter according to the present disclosure.

In the embodiment of FIG. 6 the method does not need to iterate back andforth between beamformed data (i.e. parameter estimates) and RF signaldata (i.e. observed data) and can therefore be computationally lessexpensive (for example in view of the required time, resources, memory,power, etc.). In particular, the method may process merely beamformeddata, i.e. operate merely with beamformed data.

It is possible to avoid iterating back and forth between beamformed dataand RF signal data, since beamforming is a linear process. Therefore,removing a linear combination of signals from beamformed data isequivalent to removing a specific signal from RF data. Moreover, forpredetermined or known characteristics of a transducer device (e.g. thegeometry of the transducer array, the type of wave etc.) it is possibleto predict which reflectors are going to impact a specific pixel, or ingeneral terms, which second special region can cause clutter in a firstset of beamformed data associated with a first region.

The improvement of the embodiment of FIG. 6 may consist in implementingthe E-M method of FIG. 5 on beamformed data. The E step may remove alinear combination of second spatial regions (for example pixels) thatare located so that they can generate clutter on a first spatial region(for example a pixel of interest). Accordingly, the M step is combinedwith the E step such that the computationally expensive back and forthprocessing of beamforming and inverse beamforming can be avoided.

Accordingly, the embodiment of FIG. 6 may save a significant amount ofcomputation operations because it implies no need to back-project thepixels from the image to the RF data matrix, i.e. to inverse thebeamforming process.

FIG. 6 schematically illustrates two transducer elements 20 c, 20 d andrespective isochronous reception areas (schematically indicated byexemplary parabolas 60 a, 60 b). As further shown, pixel 40 a associatedwith a respective region in the medium lies on parabola 60 a, pixel 40 cassociated with a respective region in the medium lies on parabola 60 b,and pixel 40 b associated with a respective region in the medium lies onthe overlapping section of parabolas 60 a and 60 b. The principles ofestimating clutter may correspond to those described above in context ofFIG. 2 . However, in context of FIG. 6 it is referred to the beamformeddata, i.e. to a first and a second set of beamformed data according tothe present disclosure. In the example of FIG. 6 , these first andsecond sets may be respective pixel 40 a, 40 b

The signal data received from for example a first region associated withthe first pixel 40 a may be isochronous to signal data received from asecond region associated with the second pixel 40 b. Hence, since thesignals associated with the respective first and second beamformed pixelmay have the same propagation time at the transducer element 20 c, thesignals associated with the second pixel may cause clutter at the firstbeamformed pixel.

More generally, any pixel located on the parabola 60 a may implyisochronous signal data for the (selected) first pixel 40 a. Hence,these pixels may be determined as being associated with second spatialregions of the medium that are located such that they can cause clutterat the first pixel 40 a.

For each of these pixels the clutter contribution may be estimated as afunction of the amplitude or intensity of the determined pixels on theparabola 60 a.

In a further option, in order to reduce computational power, only suchpixels located on the parabola 60 a may be considered for estimating theclutter, whose amplitude exceeds a predefined threshold. This maysimplify the method and advantageously reduce computational costs.

Furthermore, in order to reduce further the clutter, the compensationmethod may be carried out in a plurality of iterations.

Instead of single pixels also groups or clusters of pixels may beconsidered as a set of beamformed data in the method.

A corresponding exemplary scenario is shown for transducer element 20 din view of pixels 40 a and 40 c being located on a parabola 60 brelevant for the transducer element 20 d. Accordingly, for transducerelement 20 b the clutter caused by a third region associated with pixel40 c may be estimated at pixel 40 a. In other words, in order toestimate the clutter at pixel 40 a, a plurality of transducer elements20 c, 20 d may be considered. Said plurality of transducer elements maycomprise all transducer elements of the transducer device or only thosetransducer elements whose signal data are used for determining pixel 40a (cf. also the iteration over loop L2, as explained above).

The embodiment of FIG. 6 may correspond to the method described incontext of FIGS. 1 and 2 and may comprise any of the features describedin the context of FIGS. 1 and 2 .

The second spatial regions may also be determined according to thefollowing example. In said example, a medium is insonified by means of alinear array that generates successive plane waves with varying incidentangle. Furthermore, the response sequence of the medium received by thelinear transducer array is processed to obtain two-dimensionalbeamformed data. In other words, the beamformed may be in the form ofpixels in two-dimensional matrix. Each pixel may correspond to a set ofbeamformed data according to present disclosure (for example an in-phaseand a quadrature phase, IQ, value).

M(x₀, z₀) may be a first spatial region associated with a firstbeamformed IQ data set according to the present disclosure. M(x₀, z₀)then refer to the pixel associated to this first spatial region. Then,the location of the second spatial regions that may cause clutter at thefirst. N(x, z) refer to such second spatial regions. By construction, Nand M share the same propagation time for a given transmitted angledplane wave θ_(in) and a given receive transducer elementP_(out)(x_(out), z_(out)=0). In the following, the medium speed of soundis assumed constant and equal to c. The following demonstration aims atcomputed the coordinates (x, z) of point N that validate the aboveconditions.

The transmit propagation time t_(in) required for the plane wave ofangle θ_(in) to reach the point M(x₀, z₀) can be expressed as, cf.equation (3):

$\begin{matrix}{{{t_{in}\left( {\theta_{in},x,z} \right)} = {\frac{1}{c}\left( {{x_{0}{\sin\left( \theta_{in} \right)}} + {z_{0}{\cos\left( \theta_{in} \right)}}} \right)}},} & (3)\end{matrix}$

The receive propagation time t_(out) required for echoes generated atpoint M(x₀, z₀) to reach the transducer P(u_(out), 0) can be expressedas, cf. equation (4):

$\begin{matrix}{{t_{out}\left( {x_{out},x,z} \right)} = {\frac{1}{c}{\sqrt{\left( {x_{0} - x_{out}} \right)^{2} + z_{0}}.}}} & (4)\end{matrix}$

The round-trip time of flight t₀ of echoes generated at point M(x₀, z₀)and measured by transducer P_(out)(x_(out), z_(out)=0) can then beexpressed as, cf. equation (5):

$\begin{matrix}{{t\left( {x_{0},y_{0},\theta_{in},x_{out}} \right)} = {{\frac{1}{c}\left( {{x_{0}{\sin\left( \theta_{in} \right)}} + {z_{0}{\cos\left( \theta_{in} \right)}} + \sqrt{\left( {x_{0} - x_{out}} \right)^{2} + z_{0}^{2}}} \right)} = {t_{0}.}}} & (5)\end{matrix}$

By definition, N(x, z) generates clutter at the pixel M(x₀, z₀).Consequently, M and N are isochronous, meaning that they share the sameround-trip propagation time for the given plane wave θ_(in) and receivedtransducer P_(out). As a result, the coordinates (x, z) are solution ofthe following equation (6):

$\begin{matrix}{{t\left( {x,z,\theta_{in},x_{out}} \right)} = {{\frac{1}{c}\left( {{x{\sin\left( \theta_{in} \right)}} + {z{\cos\left( \theta_{in} \right)}} + \sqrt{\left( {x - x_{out}} \right)^{2} + z^{2}}} \right)} = {t_{0}.}}} & (6)\end{matrix}$

After development, (Eq. (6)) can be expressed as, cf. equation (7):

x ²(1−sin(θ_(in))²)+z ²(1−cos²(θ_(in)))+2×(ct ₀ sin(θ_(in))−x_(out))+2zct ₀ cos(θ_(in))−

2xz cos(θ_(in))sin(θ_(in))+x _(out) ² −c ² t ₀ ²=0   (7).

This equation corresponds to a quadratic curve. One may compute thedeterminant of the matrix of the quadratic J is null, cf. equation (8):

J=(1−sin(θ_(in))²)(1−cos²(θ_(in)))−(−cos(θ_(in))sin(θ_(in)))²=0.  (8).

This characteristic ensure that the quadratic curve is a parabola.

In a first exemplary case, where the angle θ_(in) of the plane wave iszero, (Eq. (7)) can be simplified and the z coordinate of the secondregion may be determined as a function the x coordinate through thefollowing equation (9):

$\begin{matrix}{z = {\frac{{- x^{2}} + {2{xx}_{out}} - x_{out}^{2} + {c^{2}t_{0}^{2}}}{2{ct}_{0}}.}} & (9)\end{matrix}$

This equation corresponds to a parabola curve. Only point N(x, z) whosecoordinates validate the above equation (9) should be considered aspotential source of clutter at the first data set corresponding to thespatial region M(x₀, z₀).

In a second exemplary case, the general problem may be considered. Firsta change of coordinates may be performed. The coordinate system may berotated by an angle θ and a new coordinate system (x′, z′) may beobtained which can be described by, cf. equation (10) and (11):

x=x′ cos(θ)+z′ sin(θ)  (10)

z=−x′ sin(θ)+z′ cos(θ)  (11)

By using these new coordinates, (Eq. 7) can be simplified and z′ can beexpressed as a function of x′, cf. equation (12):

$\begin{matrix}{z_{cl}^{\prime} = \frac{{x^{\prime 2}\left( {{\cos^{4}(\theta)} + {\sin^{4}(\theta)}} \right)} - {2x^{\prime x_{out}}{\cos(\theta)}} + x_{out}^{2} - {c^{2}t_{0}^{2}}}{{2\sin(\theta)x_{out}} - {ct}_{0}}} & (12)\end{matrix}$

Once x′ and z′ have been determined, it may be reverted to x and z ifnecessary, by a rotation of the angle −θ.

Throughout the description, including the claims, the term “comprisinga” should be understood as being synonymous with “comprising at leastone” unless otherwise stated. In addition, any range set forth in thedescription, including the claims should be understood as including itsend value(s) unless otherwise stated. Specific values for describedelements should be understood to be within accepted manufacturing orindustry tolerances known to one of skill in the art, and any use of theterms “substantially” and/or “approximately” and/or “generally” shouldbe understood to mean falling within such accepted tolerances.

Although the present disclosure herein has been described with referenceto particular embodiments, it is to be understood that these embodimentsare merely illustrative of the principles and applications of thepresent disclosure.

It is intended that the specification and examples be considered asexemplary only, with a true scope of the disclosure being indicated bythe following claims.

A reference herein to a patent document or any other matter identifiedas prior art, is not to be taken as an admission that the document orother matter was known or that the information it contains was part ofthe common general knowledge as at the priority date of any of theclaims.

1. A method for processing beamformed data of a medium, the beamformeddata comprising a first set of beamformed data associated with a firstspatial region and a second set of beamformed data associated with asecond spatial region, wherein the method comprises: estimating cluttercaused by the second spatial region at the first set.
 2. The methodaccording to claim 1, further comprising: selecting the first set anddetermining the second set as a function of the location of theassociated second spatial region, wherein the second spatial region islocated such that the first set is susceptible for clutter generated atthe second spatial region, and/or selecting the second set anddetermining the first set as a function of the location of theassociated first spatial region, wherein the first spatial region islocated such that the first set is susceptible for clutter generated atthe second spatial region, and/or compensating the estimated clutter atthe first set, and/or removing the clutter at the first set.
 3. Themethod according to claim 1, wherein at least one of: the clutter isestimated as a function of at least one of the location of the firstspatial region and the location of the second spatial region, theclutter is estimated as a function of the second set and/or theamplitude of the second set, and the second set is considered forestimating the clutter, when the amplitude of the second set exceeds apredefined threshold.
 4. The method according to claim 1, wherein: thesecond set of beamformed data is associated to signal data received fromthe medium which is isochronous to signal data received from the mediumassociated with the first set.
 5. The method according to claim 2,wherein: determining the second set of beamformed data comprises:determining a plurality of second sets of beamformed data respectivelyassociated with a plurality of second spatial regions that are locatedsuch that the first set is susceptible for clutter generated at thesecond spatial regions, and estimating the clutter at the first setcomprises: estimating a plurality of clutter contributions respectivelyassociated to the second sets, the clutter at the first set being afunction of the plurality of clutter contributions.
 6. The methodaccording to claim 1, wherein the clutter at the first spatial region isestimated by a linear combination of the plurality of cluttercontributions at the first spatial region.
 7. The method according toclaim 1, further comprising before selecting at least one of the firstand the second set: processing ultrasound signal data of the medium toobtain the beamformed data.
 8. The method according to claim 1, furthercomprising before processing ultrasound signal data or selectingbeamformed data: transmitting an emitted sequence of ultrasound wavesinto the medium, and receiving a response sequence of ultrasound wavesfrom the medium, wherein the ultrasound signal data are based on theresponse sequence of ultrasound waves.
 9. The method according to claim1, wherein at least one of the first and second set is furtherdetermined and/or the clutter is further estimated as a function of atleast one of: the geometry of a transducer device used for acquiringdata of the medium on which the beamformed data are based, thearrangement and/or the size of the single transducer elements of thetransducer device, at least one of the emission and receive aperture ofthe transducer device, the emission duration, the wavelength and/or typeof emission pulse on which the beamformed data is based, the geometry ofthe emitted wave front, and a predetermined speed of sound model of themedium.
 10. The method according to claim 1, wherein at least one of:each set of beamformed data is associated with at least one pixel orvoxel, and the beamformed data are in-phase and quadrature phase, IQ,data and/or radio frequency, RF, data.
 11. Method according to claim 1,wherein at least one of the first set, the second set, the first spatialregion and the second spatial region is predetermined.
 12. The methodaccording to claim 1, wherein the method is performed for a plurality offirst spatial regions of the medium, in parallel and/or in series. 13.The method according to claim 1, wherein the method is performed for thefirst spatial region in several iterations, wherein at each iterationmodified beamformed data is obtained by compensating the estimatedclutter, wherein the modified beamformed data obtained in a firstiteration is used in a subsequent second iteration.
 14. A method oftraining an artificial intelligence-based model (AI) based on anestimated clutter according to the method of claim
 1. 15. A method forprocessing beamformed data of a medium, the method comprising: using theAI based model of claim 14 to estimate an amount clutter and/orcompensate an estimated clutter.
 16. A computer program comprisingcomputer-readable instructions which when executed by a data processingsystem cause the data processing system to carry out the methodaccording to claim
 1. 17. A system for processing beamformed data of amedium, the beamformed data comprising a first set of beamformed dataassociated with a first spatial region and a second set of beamformeddata associated with a second spatial region, wherein the systemcomprises a processing unit configured to: estimate the clutter causedby the second spatial region at the first set.